{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.concat([pd.read_csv(\"Youtube01-Psy.csv\"),\n",
    "               pd.read_csv(\"Youtube02-KatyPerry.csv\"),\n",
    "               pd.read_csv(\"Youtube03-LMFAO.csv\"),\n",
    "               pd.read_csv(\"Youtube04-Eminem.csv\"),\n",
    "               pd.read_csv(\"Youtube05-Shakira.csv\")])\n",
    "d = d.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kfold = StratifiedKFold(n_splits=5)\n",
    "splits = kfold.split(d, d['CLASS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Split\n",
      "[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
      " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
      " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
      " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
      " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
      " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
      " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
      " 378 379 380 381 382 383 384 386 387 388 389 390 391 392]\n",
      "Split\n",
      "[385 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409\n",
      " 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427\n",
      " 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445\n",
      " 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463\n",
      " 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481\n",
      " 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499\n",
      " 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517\n",
      " 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535\n",
      " 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553\n",
      " 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571\n",
      " 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589\n",
      " 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607\n",
      " 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625\n",
      " 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643\n",
      " 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661\n",
      " 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679\n",
      " 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697\n",
      " 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715\n",
      " 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733\n",
      " 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751\n",
      " 752 753 754 755 756 758 760 762 764 766 767 768 769 770 774 775 778 779\n",
      " 780 782 785 786 788 790 792 795 797 800 805 806 809]\n",
      "Split\n",
      "[ 757  759  761  763  765  771  772  773  776  777  781  783  784  787  789\n",
      "  791  793  794  796  798  799  801  802  803  804  807  808  810  811  812\n",
      "  813  814  815  816  817  818  819  820  821  822  823  824  825  826  827\n",
      "  828  829  830  831  832  833  834  835  836  837  838  839  840  841  842\n",
      "  843  844  845  846  847  848  849  850  851  852  853  854  855  856  857\n",
      "  858  859  860  861  862  863  864  865  866  867  868  869  870  871  872\n",
      "  873  874  875  876  877  878  879  880  881  882  883  884  885  886  887\n",
      "  888  889  890  891  892  893  894  895  896  897  898  899  900  901  902\n",
      "  903  904  905  906  907  908  909  910  911  912  913  914  915  916  917\n",
      "  918  919  920  921  922  923  924  925  926  927  928  929  930  931  932\n",
      "  933  934  935  936  937  938  939  940  941  942  943  944  945  946  947\n",
      "  948  949  950  951  952  953  954  955  956  957  958  959  960  961  962\n",
      "  963  964  965  966  967  968  969  970  971  972  973  974  975  976  977\n",
      "  978  979  980  981  982  983  984  985  986  987  988  989  990  991  992\n",
      "  993  994  995  996  997  998  999 1000 1001 1002 1003 1004 1005 1006 1007\n",
      " 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022\n",
      " 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037\n",
      " 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052\n",
      " 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067\n",
      " 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082\n",
      " 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097\n",
      " 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112\n",
      " 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127\n",
      " 1128 1129 1130 1131 1132 1133 1134 1135 1137 1138 1141 1142 1145 1146 1149\n",
      " 1152 1154 1155 1156 1157 1159 1161 1162 1163 1164 1165 1168 1170 1173 1174\n",
      " 1175 1177 1178 1179 1182 1183 1185 1186 1188 1189 1191 1193 1195 1196 1197\n",
      " 1199]\n",
      "Split\n",
      "[1136 1139 1140 1143 1144 1147 1148 1150 1151 1153 1158 1160 1166 1167 1169\n",
      " 1171 1172 1176 1180 1181 1184 1187 1190 1192 1194 1198 1200 1201 1202 1203\n",
      " 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218\n",
      " 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233\n",
      " 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248\n",
      " 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263\n",
      " 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278\n",
      " 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293\n",
      " 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308\n",
      " 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323\n",
      " 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338\n",
      " 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353\n",
      " 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368\n",
      " 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383\n",
      " 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398\n",
      " 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413\n",
      " 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428\n",
      " 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443\n",
      " 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458\n",
      " 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473\n",
      " 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488\n",
      " 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503\n",
      " 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518\n",
      " 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533\n",
      " 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1549\n",
      " 1550 1553 1554 1556 1557 1558 1559 1560 1562 1563 1564 1565 1567 1568 1570\n",
      " 1571]\n",
      "Split\n",
      "[1548 1551 1552 1555 1561 1566 1569 1572 1573 1574 1575 1576 1577 1578 1579\n",
      " 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594\n",
      " 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609\n",
      " 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624\n",
      " 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639\n",
      " 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654\n",
      " 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669\n",
      " 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684\n",
      " 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699\n",
      " 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714\n",
      " 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729\n",
      " 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744\n",
      " 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759\n",
      " 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774\n",
      " 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789\n",
      " 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804\n",
      " 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819\n",
      " 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834\n",
      " 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849\n",
      " 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864\n",
      " 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879\n",
      " 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894\n",
      " 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909\n",
      " 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924\n",
      " 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939\n",
      " 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954\n",
      " 1955]\n"
     ]
    }
   ],
   "source": [
    "for train, test in splits:\n",
    "    print(\"Split\")\n",
    "    print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_and_test(train_idx, test_idx):\n",
    "    \n",
    "    train_content = d['CONTENT'].iloc[train_idx]\n",
    "    test_content = d['CONTENT'].iloc[test_idx]\n",
    "    \n",
    "    tokenizer = Tokenizer(num_words=2000)\n",
    "    \n",
    "    # learn the training words (not the testing words!)\n",
    "    tokenizer.fit_on_texts(train_content)\n",
    "\n",
    "    # options for mode: binary, freq, tfidf\n",
    "    d_train_inputs = tokenizer.texts_to_matrix(train_content, mode='tfidf')\n",
    "    d_test_inputs = tokenizer.texts_to_matrix(test_content, mode='tfidf')\n",
    "\n",
    "    # divide tfidf by max\n",
    "    d_train_inputs = d_train_inputs/np.amax(np.absolute(d_train_inputs))\n",
    "    d_test_inputs = d_test_inputs/np.amax(np.absolute(d_test_inputs))\n",
    "\n",
    "    # subtract mean, to get values between -1 and 1\n",
    "    d_train_inputs = d_train_inputs - np.mean(d_train_inputs)\n",
    "    d_test_inputs = d_test_inputs - np.mean(d_test_inputs)\n",
    "\n",
    "    # one-hot encoding of outputs\n",
    "    d_train_outputs = np_utils.to_categorical(d['CLASS'].iloc[train_idx])\n",
    "    d_test_outputs = np_utils.to_categorical(d['CLASS'].iloc[test_idx])\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Dense(512, input_shape=(2000,)))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(2))\n",
    "    model.add(Activation('softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adamax',\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    model.fit(d_train_inputs, d_train_outputs, epochs=10, batch_size=16)\n",
    "\n",
    "    scores = model.evaluate(d_test_inputs, d_test_outputs)\n",
    "    print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1564/1564 [==============================] - 3s 2ms/step - loss: 0.5992 - acc: 0.7986\n",
      "Epoch 2/10\n",
      "1564/1564 [==============================] - 1s 582us/step - loss: 0.3709 - acc: 0.9137\n",
      "Epoch 3/10\n",
      "1564/1564 [==============================] - 1s 582us/step - loss: 0.2382 - acc: 0.9425\n",
      "Epoch 4/10\n",
      "1564/1564 [==============================] - 1s 581us/step - loss: 0.1761 - acc: 0.9520\n",
      "Epoch 5/10\n",
      "1564/1564 [==============================] - 1s 575us/step - loss: 0.1457 - acc: 0.9584\n",
      "Epoch 6/10\n",
      "1564/1564 [==============================] - 1s 606us/step - loss: 0.1224 - acc: 0.9636\n",
      "Epoch 7/10\n",
      "1564/1564 [==============================] - 1s 581us/step - loss: 0.1093 - acc: 0.9680\n",
      "Epoch 8/10\n",
      "1564/1564 [==============================] - 1s 598us/step - loss: 0.0979 - acc: 0.9712\n",
      "Epoch 9/10\n",
      "1564/1564 [==============================] - 1s 599us/step - loss: 0.0890 - acc: 0.9738\n",
      "Epoch 10/10\n",
      "1564/1564 [==============================] - 1s 585us/step - loss: 0.0794 - acc: 0.9770\n",
      "392/392 [==============================] - 1s 1ms/step\n",
      "acc: 97.19%\n",
      "Epoch 1/10\n",
      "1565/1565 [==============================] - 3s 2ms/step - loss: 0.6007 - acc: 0.7917\n",
      "Epoch 2/10\n",
      "1565/1565 [==============================] - 1s 594us/step - loss: 0.3718 - acc: 0.9169\n",
      "Epoch 3/10\n",
      "1565/1565 [==============================] - 1s 579us/step - loss: 0.2305 - acc: 0.9431\n",
      "Epoch 4/10\n",
      "1565/1565 [==============================] - 1s 574us/step - loss: 0.1703 - acc: 0.9591\n",
      "Epoch 5/10\n",
      "1565/1565 [==============================] - 1s 609us/step - loss: 0.1361 - acc: 0.9668\n",
      "Epoch 6/10\n",
      "1565/1565 [==============================] - 1s 571us/step - loss: 0.1163 - acc: 0.9706\n",
      "Epoch 7/10\n",
      "1565/1565 [==============================] - 1s 596us/step - loss: 0.0990 - acc: 0.9751\n",
      "Epoch 8/10\n",
      "1565/1565 [==============================] - 1s 590us/step - loss: 0.0901 - acc: 0.9732\n",
      "Epoch 9/10\n",
      "1565/1565 [==============================] - 1s 574us/step - loss: 0.0795 - acc: 0.9808\n",
      "Epoch 10/10\n",
      "1565/1565 [==============================] - 1s 583us/step - loss: 0.0700 - acc: 0.9827\n",
      "391/391 [==============================] - 1s 1ms/step\n",
      "acc: 92.07%\n",
      "Epoch 1/10\n",
      "1565/1565 [==============================] - 3s 2ms/step - loss: 0.5994 - acc: 0.7840\n",
      "Epoch 2/10\n",
      "1565/1565 [==============================] - 1s 589us/step - loss: 0.3728 - acc: 0.9112\n",
      "Epoch 3/10\n",
      "1565/1565 [==============================] - 1s 578us/step - loss: 0.2366 - acc: 0.9450\n",
      "Epoch 4/10\n",
      "1565/1565 [==============================] - 1s 574us/step - loss: 0.1780 - acc: 0.9482\n",
      "Epoch 5/10\n",
      "1565/1565 [==============================] - 1s 602us/step - loss: 0.1421 - acc: 0.9617\n",
      "Epoch 6/10\n",
      "1565/1565 [==============================] - 1s 593us/step - loss: 0.1266 - acc: 0.9617\n",
      "Epoch 7/10\n",
      "1565/1565 [==============================] - 1s 595us/step - loss: 0.1126 - acc: 0.9655\n",
      "Epoch 8/10\n",
      "1565/1565 [==============================] - 1s 621us/step - loss: 0.1024 - acc: 0.9693\n",
      "Epoch 9/10\n",
      "1565/1565 [==============================] - 1s 575us/step - loss: 0.0922 - acc: 0.9770\n",
      "Epoch 10/10\n",
      "1565/1565 [==============================] - 1s 559us/step - loss: 0.0846 - acc: 0.9764\n",
      "391/391 [==============================] - 1s 2ms/step\n",
      "acc: 95.91%\n",
      "Epoch 1/10\n",
      "1565/1565 [==============================] - 3s 2ms/step - loss: 0.5936 - acc: 0.7885\n",
      "Epoch 2/10\n",
      "1565/1565 [==============================] - 1s 607us/step - loss: 0.3622 - acc: 0.9182\n",
      "Epoch 3/10\n",
      "1565/1565 [==============================] - 1s 628us/step - loss: 0.2258 - acc: 0.9489\n",
      "Epoch 4/10\n",
      "1565/1565 [==============================] - 1s 608us/step - loss: 0.1595 - acc: 0.9572\n",
      "Epoch 5/10\n",
      "1565/1565 [==============================] - 1s 602us/step - loss: 0.1233 - acc: 0.9668\n",
      "Epoch 6/10\n",
      "1565/1565 [==============================] - 1s 604us/step - loss: 0.1026 - acc: 0.9700\n",
      "Epoch 7/10\n",
      "1565/1565 [==============================] - 1s 605us/step - loss: 0.0889 - acc: 0.9712\n",
      "Epoch 8/10\n",
      "1565/1565 [==============================] - 1s 596us/step - loss: 0.0772 - acc: 0.9783 0s - loss: 0.075\n",
      "Epoch 9/10\n",
      "1565/1565 [==============================] - 1s 602us/step - loss: 0.0669 - acc: 0.9815 0s - loss: 0.068\n",
      "Epoch 10/10\n",
      "1565/1565 [==============================] - 1s 605us/step - loss: 0.0580 - acc: 0.9834\n",
      "391/391 [==============================] - 1s 2ms/step\n",
      "acc: 94.63%\n",
      "Epoch 1/10\n",
      "1565/1565 [==============================] - 3s 2ms/step - loss: 0.5903 - acc: 0.8000\n",
      "Epoch 2/10\n",
      "1565/1565 [==============================] - 1s 601us/step - loss: 0.3573 - acc: 0.9144\n",
      "Epoch 3/10\n",
      "1565/1565 [==============================] - 1s 589us/step - loss: 0.2261 - acc: 0.9457\n",
      "Epoch 4/10\n",
      "1565/1565 [==============================] - 1s 603us/step - loss: 0.1709 - acc: 0.9565\n",
      "Epoch 5/10\n",
      "1565/1565 [==============================] - 1s 600us/step - loss: 0.1382 - acc: 0.9636\n",
      "Epoch 6/10\n",
      "1565/1565 [==============================] - 1s 593us/step - loss: 0.1199 - acc: 0.9681\n",
      "Epoch 7/10\n",
      "1565/1565 [==============================] - 1s 600us/step - loss: 0.1032 - acc: 0.9732 0s - loss: 0.1033\n",
      "Epoch 8/10\n",
      "1565/1565 [==============================] - 1s 580us/step - loss: 0.0953 - acc: 0.9738\n",
      "Epoch 9/10\n",
      "1565/1565 [==============================] - 1s 578us/step - loss: 0.0859 - acc: 0.9744\n",
      "Epoch 10/10\n",
      "1565/1565 [==============================] - 1s 602us/step - loss: 0.0746 - acc: 0.9808\n",
      "391/391 [==============================] - 1s 2ms/step\n",
      "acc: 95.65%\n"
     ]
    }
   ],
   "source": [
    "kfold = StratifiedKFold(n_splits=5)\n",
    "splits = kfold.split(d, d['CLASS'])\n",
    "cvscores = []\n",
    "for train_idx, test_idx, in splits:\n",
    "    scores = train_and_test(train_idx, test_idx)\n",
    "    cvscores.append(scores[1] * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.09% (+/- 1.72%)\n"
     ]
    }
   ],
   "source": [
    "print(\"%.2f%% (+/- %.2f%%)\" % (np.mean(cvscores), np.std(cvscores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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